1,227 research outputs found
Identifying RR Lyrae in the ZTF DR3 dataset
We present a RR Lyrae (RRL) catalogue based on the combination of the third
data release of the Zwicky Transient Facility (ZTF DR3) and \textit{Gaia} EDR3.
We use a multi-step classification pipeline relying on the Fourier
decomposition fitting to the multi-band ZTF light curves and random forest
classification. The resulting catalogue contains 71,755 RRLs with period and
light curve parameter measurements and has completeness of 0.92 and purity of
0.92 with respect to the SOS \textit{Gaia} DR2 RRLs. The catalogue covers the
Northern sky with declination , its completeness is for heliocentric distance ~kpc, and the most distant RRL at
132~kpc. Compared with several other RRL catalogues covering the Northern sky,
our catalogue has more RRLs around the Galactic halo and is more complete at
low Galactic latitude areas. Analysing the spatial distribution of RRL in the
catalogue reveals the previously known major over-densities of the Galactic
halo, such as the Virgo over-density and the Hercules-Aquila Cloud, with some
evidence of an association between the two. We also analyse the Oosterhoff
fraction differences throughout the halo, comparing it with the density
distribution, finding increasing Oosterhoff I fraction at the elliptical radii
between 16 and 32 kpc and some evidence of different Oosterhoff fractions
across various halo substructures
Search for globular clusters associated with the Milky Way dwarf galaxies using Gaia DR2
We report the result of searching for globular clusters (GCs) around 55 Milky
Way satellite dwarf galaxies within the distance of 450 kpc from the Galactic
Center except for the Large and Small Magellanic Clouds and the Sagittarius
dwarf. For each dwarf, we analyze the stellar distribution of sources in Gaia
DR2, selected by magnitude, proper motion, and source morphology. Using the
kernel density estimation of stellar number counts, we identify eleven possible
GC candidates. Crossed-matched with existing imaging data, all eleven objects
are known either GCs or galaxies and only Fornax GC 1-6 among them are
associated with the targeted dwarf galaxy. Using simulated GCs, we calculate
the GC detection limit that spans the range from for distant dwarfs to for
nearby systems. Assuming a Gaussian GC luminosity function, we compute that the
completeness of the GC search is above 90 percent for most dwarf galaxies. We
construct the 90 percent credible intervals/upper limits on the GC specific
frequency of the MW dwarf galaxies: for
Fornax, for the dwarfs with , for the dwarfs with , and for
the dwarfs with . Based on , we derive the
probability of galaxies hosting GCs given their luminosity, finding that the
probability of galaxies fainter than to host GCs is lower than
0.1
Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer
Zero-shot cross-lingual transfer is a central task in multilingual NLP,
allowing models trained in languages with more sufficient training resources to
generalize to other low-resource languages. Earlier efforts on this task use
parallel corpora, bilingual dictionaries, or other annotated alignment data to
improve cross-lingual transferability, which are typically expensive to obtain.
In this paper, we propose a simple yet effective method, SALT, to improve the
zero-shot cross-lingual transfer of the multilingual pretrained language models
without the help of such external data. By incorporating code-switching and
embedding mixup with self-augmentation, SALT effectively distills cross-lingual
knowledge from the multilingual PLM and enhances its transferability on
downstream tasks. Experimental results on XNLI and PAWS-X show that our method
is able to improve zero-shot cross-lingual transferability without external
data. Our code is available at https://github.com/luka-group/SALT.Comment: AACL 202
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